FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition

• URL: http://arxiv.org/abs/2604.16649v1
• Date: Fri, 17 Apr 2026 19:11:27 GMT
• Title: FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition
• Abstract summary: Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part.<n>This paper introduces FLARE, a data-efficient surrogate modeling framework for predicting post-cooling displacement fields in DED from geometric and process parameters.<n> FLARE encodes each simulation as an implicit neural field and regularizes the corresponding neural-network weights so that they follow the affine structure of the input parameter space.
• Score: 10.969341855852276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part. Thermo-mechanical finite element simulations are widely used to estimate these effects, but their computational cost and the complexity of accurately capturing DED physics limit their use in design iteration and process optimization. This paper introduces FLARE (Field Prediction via Linear Affine Reconstruction in wEight-space), a data-efficient surrogate modeling framework for predicting post-cooling displacement fields in DED from geometric and process parameters. We develop a predefined-geometry DED simulation workflow using an open-source finite element framework and generate a dataset of simulations with varying geometry, laser power, and deposition velocity. Each simulation provides full-field displacement, stress, strain, and temperature data throughout the manufacturing process. FLARE encodes each simulation as an implicit neural field and regularizes the corresponding neural-network weights so that they follow the affine structure of the input parameter space. This enables prediction of unseen parameter combinations by reconstructing network weights through affine mixing of training examples. On this DED benchmark, the method shows improved accuracy compared to baseline methods in both in-distribution and extrapolation settings. Although the present study focuses on DED displacement prediction, the proposed affine weight-space reconstruction framework offers a promising approach for data-efficient surrogate modeling of physical fields.

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